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Large Language Models for Generative Information Extraction: A Survey

Paper · 2023 · Derong Xu, Wei Chen, Wenjun Peng et al.

Summary

Information extraction (IE) turns raw text into structured knowledge such as entities, relations and events. Traditionally this required separate, task-specific models that each needed costly annotated corpora. This survey reviews how generative large language models are being used instead: the model writes out the structured target as a sequence, guided by prompts or instructions. The authors organise the literature along two axes. The first covers IE subtasks (NER, RE, EE), handled either separately or within universal frameworks that cover several tasks at once. The second covers techniques, including prompting, fine-tuning and data augmentation, with particular attention to low-resource and zero- or few-shot settings. They also cover domain-specific applications and evaluation studies, and compare representative methods. Open problems include:

  • mismatch between free-text output and structured formats,
  • hallucination,
  • context sensitivity,
  • heavy compute needs,
  • difficulty updating model knowledge.

The paper calls itself the first survey of LLM-based generative IE. Its dataset and backbone catalogues are a practical reference for anyone building extraction pipelines.

Key points

  • Subtask taxonomy: NER is split into identification and typing. RE has three settings: classification, triplet and strict. EE is split into trigger detection and argument extraction.
  • Universal frameworks reviewed include TANL, DEEPSTRUCT, GenIE, UIE, LasUIE, ChatIE and InstructUIE.
  • Table 7 lists 50 benchmarks: 32 for NER (e.g. CoNLL03, Few-NERD with 66 classes, OntoNotes 5.0), 11 for RE (e.g. DocRED, TACRED, NYT) and 7 for EE (e.g. ACE05, RAMS, CASIE).
  • CoNLL03 contains 1,393 English and 909 German news articles annotated with four entity types.
  • Common backbones include BART, T5/mT5/Flan-T5, ChatGLM, the LLaMA family (up to LLaMA3 405B) and GPT-2 through GPT-4. Related work is tracked in the GitHub repository LLM4IE. The paper was published in Frontiers of Computer Science (DOI 10.1007/s11704-024-40555-y).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢TencentShenzhen · China134 works · 19,743 citations🎓University of Science and Technology of ChinaHefei · China108 works · 112,698 citations🎓City University of Hong KongHong Kong · China45 works · 3,014 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksInformation extraction · 167 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Information extraction · 50 works

Cite

Cite as Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang and Enhong Chen. 2023. Large Language Models for Generative Information Extraction: A Survey. arXiv:2312.17617.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2312.17617 · arXiv 2312.17617

@misc{xu2023large,
  title = {{Large Language Models for Generative Information Extraction: A Survey}},
  author = {Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang and Enhong Chen},
  year = {2023},
  doi = {10.48550/arxiv.2312.17617},
  eprint = {2312.17617},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.17617}
}

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